Sorry seems to be the hardest word : the effect of self-attribution when apologizing for a brand crisis

When apologizing for a product failure, self-attribution by a business inevitably affects consumer attitude and behavior. This study draws from the dissonance-attribution model and investigates the effect of self-attribution in apologies on consumers\u27 brand attitude. Using a 2x2 experiment, the results show that internal attribution generates significant change in brand attitude in a positive direction, while external attribution leads to negative change in brand attitude. Dispositional attribution leads to significantly more positive brand attitude than situational attribution. Internal/dispositional attribution produces significantly more positive effect on consumer attitude than the other three types of attribution. Moreover, perceived risk is found to mediate the relationship between attributions and brand attitude, and such mediating effect is moderated by consumers\u27 corporate associations. Clearly, how a company apologizes for a product crisis makes a big difference in the effectiveness of recovery strategies to restore consumer confidence

Efficient Privacy Preserving Viola-Jones Type Object Detection via Random Base Image Representation

A cloud server spent a lot of time, energy and money to train a Viola-Jones type object detector with high accuracy. Clients can upload their photos to the cloud server to find objects. However, the client does not want the leakage of the content of his/her photos. In the meanwhile, the cloud server is also reluctant to leak any parameters of the trained object detectors. 10 years ago, Avidan & Butman introduced Blind Vision, which is a method for securely evaluating a Viola-Jones type object detector. Blind Vision uses standard cryptographic tools and is painfully slow to compute, taking a couple of hours to scan a single image. The purpose of this work is to explore an efficient method that can speed up the process. We propose the Random Base Image (RBI) Representation. The original image is divided into random base images. Only the base images are submitted randomly to the cloud server. Thus, the content of the image can not be leaked. In the meanwhile, a random vector and the secure Millionaire protocol are leveraged to protect the parameters of the trained object detector. The RBI makes the integral-image enable again for the great acceleration. The experimental results reveal that our method can retain the detection accuracy of that of the plain vision algorithm and is significantly faster than the traditional blind vision, with only a very low probability of the information leakage theoretically.Comment: 6 pages, 3 figures, To appear in the proceedings of the IEEE International Conference on Multimedia and Expo (ICME), Jul 10, 2017 - Jul 14, 2017, Hong Kong, Hong Kon